Extracranial localization of intracranial interictal epileptiform activity using LORETA (low resolution electromagnetic tomography)

Lantz, Göran; Michel, Christoph M.; Pascual-Marqui, Roberto D.; Spinelli, Laurent; Seeck, Margitta; Seri, Stefano; Landis, Théodor; Rosén, Ingmar

doi:10.1016/s0921-884x(96)96551-0

Cited by 120 publications

(59 citation statements)

References 23 publications

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“…One of the methods of this kind that is widely used is called low resolution electromagnetic tomography (LORETA) [56], [93]. LORETA selects the smoothest of all possible 3-D current distributions and, in this way, it introduces a certain amount of spatial dispersion.…”

Section: From Topographical Maps To Source Modelingmentioning

confidence: 99%

The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy

Silva

2008

IEEE Rev. Biomed. Eng.

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Section: From Topographical Maps To Source Modelingmentioning

confidence: 99%

The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy

Silva

2008

IEEE Rev. Biomed. Eng.

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“…As another example, the low resolution electromagnetic tomography (LORETA) inverse method [Lantz et al, 1997;PascualMarqui et al, 1994], which imposes a smoothness constraint on the inverse solution, can also be expressed in this framework. LORETA specifies R ؊1 , instead of R:…”

Section: Appendix: Inverse Operator Derivationsmentioning

confidence: 99%

Monte Carlo simulation studies of EEG and MEG localization accuracy

Liu¹,

Dale²,

Belliveau³

2002

Human Brain Mapping

329

256

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Both electroencephalography (EEG) and magnetoencephalography (MEG) are currently used to localize brain activity. The accuracy of source localization depends on numerous factors, including the specific inverse approach and source model, fundamental differences in EEG and MEG data, and the accuracy of the volume conductor model of the head (i.e., the forward model). Using Monte Carlo simulations, this study removes the effect of forward model errors and theoretically compares the use of EEG alone, MEG alone, and combined EEG/MEG data sets for source localization. Here, we use a linear estimation inverse approach with a distributed source model and a realistic forward head model. We evaluated its accuracy using the crosstalk and point spread metrics. The crosstalk metric for a specified location on the cortex describes the amount of activity incorrectly localized onto that location from other locations. The point spread metric provides the complementary measure: for that same location, the point spread describes the mis-localization of activity from that specified location to other locations in the brain. We also propose and examine the utility of a "noise sensitivity normalized" inverse operator. Given our particular forward and inverse models, our results show that 1) surprisingly, EEG localization is more accurate than MEG localization for the same number of sensors averaged over many source locations and orientations; 2) as expected, combining EEG with MEG produces the best accuracy for the same total number of sensors; 3) the noise sensitivity normalized inverse operator improves the spatial resolution relative to the standard linear estimation operator; and 4) use of an a priori fMRI constraint universally reduces both crosstalk and point spread. Hum. Brain Mapping 16:47-62, 2002.

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“…Previous studies employing LORETA showed that the method is able to provide physiologically meaningful results, e.g. during basic visual and auditory tasks (Pascual-Marqui et al, 1994), epileptic discharges (Lantz et al, 1997), as well as cognitive tasks known to engage speci®c brain regions as assessed with other imaging techniques Strik et al, 1998).…”

Section: Brain Electric Tomographymentioning

confidence: 99%

Face-elicited ERPs and affective attitude: brain electric microstate and tomography analyses

Pizzagalli

Lehmann

Koenig³

et al. 2000

Clinical Neurophysiology

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Extracranial localization of intracranial interictal epileptiform activity using LORETA (low resolution electromagnetic tomography)

Cited by 120 publications

References 23 publications

The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy

The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy

Monte Carlo simulation studies of EEG and MEG localization accuracy

Face-elicited ERPs and affective attitude: brain electric microstate and tomography analyses

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